The key lesson of all these exercises is that you can push yourself to be better and more confident in theory by tackling simple, paradigmatic problems in an incremental way. You must put pencil to paper! You must do it regularly. But once you do, the benefits come quickly. Each minirealization builds into knowledge. Each solved simple problem builds your intuition for understanding complex systems.
Below are solutions to the problems from last time. Looking for more? Scroll down for some additional problems.
Here are the answers to the questions from last time, which explain the connection between continuous and discretetime kinetic descriptions.

Give the full definition of and explain why it’s dimensionless.
 This is the conditional to probability to be in state at time if a system was in state at time . Importantly, it’s completely irrelevant to where the system may have been in between and . It only matters where the system is at the times we’re “looking.” The probability is dimensionless because it’s simply a probability – the fractional chance to be in state – and not a probability per unit time like a rate constant.

If is the timedependent probability of state , show that .
 At time , some fraction of the probability is in each state , including . Then the total probability in state at the later time is the sum of the ‘contributions’ to state from every state indexed by (including ) based on the . More precisely, is the fraction of the state probability which ends up in , implying is the fraction ‘contributed’.

Using the exact solutions to the continuoustime twostate probabilities, , which we derived previously, calculate the discretetime transition probabilities and for transitions into state A. This problem is trickier than it sounds (though the math is easy), but don’t worry the derivation is there for you in the notes. The key trick is to write in terms of and using the fact that for any/all .
 For this, please just look at the notes. It’s written out carefully there.
Going forward, here are some key problems you should solve.
 Demonstrate the correctness of the (differential) continuity equation in one dimension by integrating the probability over a small increment.
 Derive the diffusion equation via a Taylor expansion of the timevarying probability distribution. See my online notes.
 Derive the Smoluchowski equation via a Taylor expansion of the timevarying probability distribution. See my online notes.
 Show that the stationary distribution of the Smoluchowski equation is the Boltzmann distribution.
 Show that overdamped dynamics lead to the Boltzmann distribution. For hints, see Sec. IIB of this paper by Chandler’s group.
 Derive the replicaexchange Metropolis acceptance criterion. For hints, see Ch. 12 of my textbook.
 Explain why one takes a linear average of MD or MC snapshots (equally spaced in time) to obtain a Boltzmannweighted average. See Ch. 2 of my textbook.
 Derive the ideal gas partition function from scratch. See Ch. 5 of my textbook.
Good luck! Work hard, but work slow …